The term “prognostics” refers to a particular field of science wherein failure of an apparatus can be predicted by monitoring various operational parameters, including, but not limited to environmental conditions, related to the operation of that apparatus. We are all familiar with prognostics to one degree or another. For example, prognostics are commonly used in automobiles. By monitoring the pressure at which oil is circulated throughout the engine, an indicator can be developed which will identify when the oil pump in the car is on the verge of failing. As a user, we can appreciate that when the oil pressure falls below what we consider a normal level, there must be something wrong with the oil pump. A prudent user will then have the oil pump serviced by a technician. Of course, this is a very simplistic example of how prognostics are used to predict a failure.
A somewhat more sophisticated example of prognostics can be found on an airplane. Various sensors continuously record temperature and vibration, along with other factors, that a jet engine experiences during operation. It is not practical to require a pilot to make a maintenance decision, for example for a jet engine, based on a wide assortment of temperature, vibration and other factors. In an actual aircraft system, this information is monitored by a computer system. The computer system consults a failure prediction model in order to predict when the jet engine either requires maintenance or is on the verge of failure. There are two predominant forms of failure prediction models used to support prognostic analysis. One type of failure prediction model is a purely theoretical failure prediction model. A purely theoretical failure prediction model is developed by analyzing the various components included in an assembly and applying specialized knowledge with respect to how these components might fail when subjected to various environmental stress factors. Another form of failure prediction model is known as an empirical failure prediction model. An empirical failure prediction model is developed by subjecting a qualification version of an assembly to various environmental stress factors. As the qualification version of the assembly is subjected to these various environmental stress factors, stress factors are recorded and various actual failures that may occur are recorded and correlated with the amount of environmental stress factor to which the assembly has been subjected.
Yesterday, in a co-owned application, Palmer et al. described a method and apparatus for predicting failure through the use of a sacrificial sensor. Using the sacrificial sensor, the sensitivity of failure of an assembly is correlated with failures exhibited by a sacrificial sensor as an entire assembly is exposed to various environmental stress factors. The use of a sacrificial sensor is clearly a vast improvement when considering the field of prognostics as a whole. However, even the use of the sacrificial sensor has its drawbacks.
A sacrificial sensor is typically sensitive to varying degrees of exposure to an environmental stress factor. In order to improve a failure prediction based on changes exhibited by a sacrificial sensor, the sacrificial sensor must be capable detecting numerous discrete levels of exposure to a particular environmental stress factor. Again, this is an important advance in the field of prognostics. However, in order to achieve even higher levels of predictive fidelity, a sacrificial sensor may need to be sensitive to hundreds, if not thousands of distinct fragility levels.